One way of preserving object appearance while changing context is image editing. An intuitive approach is through user-provided text prompts. Text-driven image manipulation has recently achieved significant progress using GANs
 [1]}, [2]}, [3]}, [4]}, [5]}, which are known for their high-quality generation, in parallel with the advent of CLIP [6]}, which consists of a semantically rich joint image-text representations.
Recent work [7]}, [8]}, [9]}, [10]} combines these components to produce highly realistic manipulations using text without needing extra manual labor.
Bau et al. [11]} further demonstrated how to use masks provided by the user, to localize the text-based editing and restrict the change to a specific spatial region.
However, while GAN-based image editing approaches succeed on highly-curated datasets [12]}, e.g., human faces, they struggle over diverse datasets with many subject types.
To obtain more expressive generation capabilities, Crowson et al. [13]} use VQ-GAN [14]}, trained over diverse data, as a backbone.
Other works [15]}, [16]} exploit the recent Diffusion models [17]}, [18]}, [19]}, [17]}, [21]}, [22]}, [23]}, [24]}, [25]}, [26]}, which achieve state-of-the-art generation quality over highly diverse datasets, often surpassing GANs [27]}.
Kim et al. [16]} show how to perform global changes, whereas Avrahami et al. [15]} successfully perform local manipulations using user-provided masks for guidance.
While most works that require only text (i.e., no masks) are limited to global editing [13]}, [31]},
Bar-Tal et al. [32]} proposed a text-based localized editing technique without using masks, showing impressive results.
While most of these editing approaches allow modification of global properties or local editing of a given image, none of them enables generating novel renditions of a given subject in new contexts.
